The process known as road-mapping is used in the selective catheterization of vessels in the framework of an interventional treatment. In these minimally invasive angiographic interventions, a 3-dimensional (3D) image of a patient's vasculature is obtained for use as a “road-map,” to assist a physician to efficiently guide or navigate instruments such as catheters or guide-wires through the patient's vasculature. Intravascular procedures are often performed using a C-arm x-ray imaging system which includes an adjustable x-ray source and an x-ray detector.
Image based navigation using road-mapping processes utilize three types of images: vessel images (3D road map of patient's vasculature), fluoroscopy images and navigation images. The vessel images are acquired by injecting a contrast agent into the blood stream during x-ray acquisition to visualize the patient's vasculature. This image is retained as a “road map” image. Fluoroscopic images are later acquired without the injection of a contrast agent and typically depict dense structures such as bones and instruments. The navigation image is computed by combining the vessel image with the fluoroscopy image to visualize the live instruments in the context of the previously-imaged vasculature. Road mapping processes find application in both two-dimensional (2D) and 3D image based navigation processes.
In 2D image processing applications, a series of vessel images is acquired by injecting contrast agent into the blood to opacify the vessel tree during image acquisition. A C-arm (on which an x-ray source is mounted) is kept stationary during the acquisition. One vessel image is selected to serve as a so-called “road map”. Navigation images are created by combining the single road map image with subsequent live fluoroscopic images for guiding instrumentation (e.g., guide wires, catheters) throughout the vasculature, in real-time.
In 3D image processing applications, contrast agent is injected to opacify the vessels, during which a C-arm is rotated on a circular trajectory around the patient. In this way a series of images are acquired that depict the vasculature from a different angle. In a subsequent processing step, the acquired images are sent to a reconstruction unit, where a 3D image (referred to as a “volume”) of the vessel tree is computed. This 3D image is retained and serves as the vessel image of the 3D road-mapping process. A 2D vessel image showing the vasculature from any angle can be obtained by re-projecting the 3D vessel image and displaying it on a graphical display. Similar to the 2D road-mapping process, this re-projected 2D vessel image is combined with subsequent live fluoroscopic images to obtain the navigation image. Due to vessel self-occlusion and blending that can occur when overlaying two 2D images, however, desired depth cues, such as highlights and shadows are often diminished, leaving the user with an image that provides limited depth perception.
If a guidewire could be “reconstructed” in 3D from a single fluoroscopic image, visualization of the guidewire position within a vessel tree can be improved. For example, 3D depth cues could be preserved by rendering the vessel tree transparent and showing the 3D guidewire inside. Also, vessels not relevant to the instant navigation task could be “pruned,” (i.e., eliminated from the rendering), thereby reducing self-occlusion and enhancing the overall image. Furthermore, a working view could be adjusted without adjusting the C-arm.
Thus, there is a need for an improved system and method for guidewire reconstruction in 3D for use during interventional medical procedures that are more advanced than current 2D imaging methods, and that provide enhanced guidewire visualization with respect to a patient's vasculature.